Abstract:
A controller for performing a peer analysis of a predictive model for a building including processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include comparing model parameters of a set of predictive models corresponding to a set of building zones, the model parameters indicating system dynamics of the building zones, to determine whether any of the building zones are exhibiting abnormal system dynamics. The operations include initiating a corrective action in response to determining that at least one of the building zones is exhibiting abnormal system dynamics.
Abstract:
A model predictive maintenance system for building equipment that performs operations including obtaining an objective function defining a cost of operating and performing maintenance on the equipment as a function of operating and maintenance decisions for time steps within a time period of a life cycle horizon and including performing a first computation of the objective function under a first scenario where maintenance is performed on the equipment during the period, a result of the first computation indicating a first cost. The operations include performing a second computation under a second scenario in which maintenance is not performed during the period, a result indicating a second cost. The operations include initiating an automated action to perform maintenance on the equipment in accordance with decisions defined by the first scenario if the first cost is less than or equal to the second cost.
Abstract:
A building energy system includes equipment and an asset allocator configured to determine an optimal allocation of energy loads across the equipment over a prediction horizon. The asset allocator generates several potential scenarios and generates an individual cost function for each potential scenario. Each potential scenario includes a predicted load required by the building and predicted prices for input resources. Each individual cost function includes a cost of purchasing the input resources from utility suppliers. The asset allocator generates a resource balance constraint and solves an optimization problem to determine the optimal allocation of the energy loads across the equipment. Solving the optimization problem includes optimizing an overall cost function that includes a weighted sum of individual cost functions for each potential scenario subject to the resource balance constraint for each potential scenario. The asset allocator controls the equipment to achieve the optimal allocation of energy loads.
Abstract:
A controller for HVAC equipment stores a cascaded model that includes a disturbance model configured to predict a heat disturbance affecting the building zone as a function of one or more exogenous parameters and a physics model configured to predict a temperature of the building zone as a function of the heat disturbance and an amount of heating or cooling provided to the building zone by HVAC equipment. The processing circuit is configured to execute a combined training procedure to determine parameters of the disturbance model and parameters of the physics model, generate control signals for the HVAC equipment using the disturbance model to predict the heat disturbance and applying the heat disturbance as an input to the physics model, and operate the HVAC equipment to provide the heating or cooling to the building zone in accordance with the control signals.
Abstract:
A system for measuring and verifying energy savings resulting from energy conservation measures in a building includes one or more energy meters and a controller. The energy meters are configured to measure an actual amount of building energy usage The controller is configured to determine an actual amount of energy savings resulting from the energy conservation measures during the measurement and verification period and to calculate a least amount of energy savings resulting from the energy conservation measures. The least amount of energy savings is a lower confidence bound on the actual amount of energy savings. The controller is configured to cause a building management system for the building to stop performing a measurement and verification process before a normal end of the measurement and verification period in response to the least amount being greater than a target amount of energy savings to be achieved by the energy conservation measures.
Abstract:
An environmental control system of a building including a first building device operable to affect environmental conditions of a zone of the building by providing a first input to the zone. The system includes a second building device operable to independently affect a subset of the environmental conditions by providing a second input to the zone and further includes a controller including a processing circuit. The processing circuit is configured to perform an optimization to generate control decisions for the building devices. The optimization is performed subject to constraints for the environmental conditions and uses a predictive model that predicts an effect of the control decisions on the environmental conditions. The processing circuit is configured to operate the building devices in accordance with the control decisions.
Abstract:
A model predictive maintenance system for building equipment including one or more processing circuits including processors and memory storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include obtaining an objective function that defines a cost of operating the building equipment and performing maintenance on the building equipment as a function of operating decisions and maintenance decisions for the building equipment for time steps within a time period. The operations include performing an optimization of the objective function to generate a maintenance and replacement strategy for the building equipment over a duration of an optimization period. The operations include estimating a savings loss predicted to result from a deviation from the maintenance and replacement strategy. The operations include adjusting an amount of savings expected to be achieved by energy conservation measures for the building equipment based on the savings loss.
Abstract:
An energy optimization system for participating in a capacity market program (CMP) includes one or more memory devices storing instructions, that, when executed on one or more processors, cause the one or more processors to receive a nominated capacity value, generate an objective function and one or more CMP constraints, wherein the one or more CMP constraints cause an optimization of the objective function with the CMP constraints to generate a resource allocation that reduces the load of the facility by the nominated capacity value in response to receiving the dispatch from the utility, receive the dispatch from the utility, optimize the objective function based on the nominated capacity value, the dispatch, and the one or more CMP constraints to determine the resource allocation, and control one or more pieces of building equipment based on the determined resource allocation.
Abstract:
A building energy system includes equipment configured to consume, store, or discharge one or more energy resources purchased from a utility supplier. At least one of the energy resources is subject to a demand charge. The system further includes a controller configured to determine an optimal allocation of the energy resources across the equipment over a demand charge period. The controller includes a stochastic optimizer configured to obtain representative loads and rates for the building or campus for each of a plurality of scenarios, generate a first objective function comprising a cost of purchasing the energy resources over a portion of the demand charge period, and perform a first optimization to determine a peak demand target for the optimal allocation of the energy resources. The peak demand target minimizes a risk attribute of the first objective function over the plurality of the scenarios. The controller is configured to control the equipment to achieve the optimal allocation of energy resources.
Abstract:
A controller for operating building equipment of a building. The controller includes one or more processors. The controller includes one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include comparing one or more model parameters of predictive models describing zones of the building to determine one or more zone groups for the building. The operations include generating one or more zone group models corresponding to the one or more zone groups. The operations include operating the building equipment using the one or more zone group models to affect a variable state or condition of the building.